Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future ...

This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...

A Look at Inference Behind the Hood The textbook example of inference is your interaction with something like a ChatGPT. From a user perspective there are only two phases. The first one is you type in ...

The world of artificial intelligence and machine learning (AI/ML) is fragmented into different domains. Two of these domains represent splits between training and inference, and cloud versus edge.

Animals survive in changing and unpredictable environments by not merely responding to new circumstances, but also, like humans, by forming inferences about their surroundings—for instance, squirrels ...

There are an increasing number of ways to do machine learning inference in the datacenter, but one of the increasingly popular means of running inference workloads is the combination of traditional ...

Inference is rapidly emerging as the next major frontier in artificial intelligence (AI). Historically, the AI development and deployment focus has been overwhelmingly on training with approximately ...